Lethargic Man (anag.)

https://allthingslinguistic.com/post/182878737498

kelseythelinguist:

You might remember a spate of news stories last year about Google Translate spitting out ominous chunks of religious prophecy when presented with nonsense words and phrases to translate. Clickbait sites suggested it might be a conspiracy, but no, it was just Google’s machine learning systems getting confused and falling back on the data they were trained on: religious texts.

But as the head of Google Translate, Macduff Hughes, told The Verge recently, machine learning is what makes Google’s ever-useful translation tools really sing. Free, easy, and instantaneous translation is one of those perks of 21st century living that many of us take for granted, but it wouldn’t be possible without AI.

Something curious that happened last year with Google Translate was people discovering that if you inputted nonsense words it would spit out snippets of religious text. […] Why did it happen? 

Usually it’s because the language you’re translating to had a lot of religious text in the training data. For every language pair we have, we train using whatever we can find on the world wide web. So the typical behavior of these models is that if it gets gibberish in, it picks out something that’s common in the training data on the target side, and for many of these low-resource languages — where there’s not a lot of text translated on the web for us to draw on — what is produced often happens to be religious.

Some languages, the first translated material we found were translations of the Bible. We take whatever we can get and that’s usually fine, but in a case where gibberish goes in, often this is the result. If the underlying translation data had been legal documents, the model would have produced legalese; if it was aircraft flight instruction manuals, it would have produced aircraft flight instructions.

Read the whole interview

https://allthingslinguistic.com/post/182878737498

https://allthingslinguistic.com/post/182834030472

https://allthingslinguistic.com/post/182834030472

https://allthingslinguistic.com/post/182811647857

January 2019 linkfest and updates: Emoji kids, book cards, and #LSA2019

I wrote an article for Wired about preliterate kids texting with emoji. Plus, some bonus adorable examples that didn’t fit in the article.

I wrote an update post about how my book on internet language is going! The book now has a publicist, Shailyn Tavella and you can email her at stavella@prh.com for questions about review copies and interviews.

I also dropped by the fancy new Riverhead office…

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https://allthingslinguistic.com/post/182811647857

https://allthingslinguistic.com/post/182807017087

Belated December 2018 linkfest and summary post! BECAUSE INTERNET preorders, Adelaide, Spain, and the most phonetic restaurant name

The preorder link for my book went up this month! You can now preorder Because Internet as a delightful surprise for your future self and to let the publisher know that people are interested in internet linguistics, either online by following the links here or by contacting your local indie bookstore. (Want a hint of what’s inside? Today in Things I Tell My Copyeditor: “stet, this is a Unicode…

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https://allthingslinguistic.com/post/182807017087

https://allthingslinguistic.com/post/182746000314

The true goal of text prediction can’t be as simple as anticipating what a user might want to type. After all, people often type things about sex or death—according to Google Ngrams, “job” is the most common noun after “blow,” and “bucket” is very common after “kick the.” But I experimentally typed these and similar taboo-but-common phrases into my phone’s keyboard, and it never predicted them straightaway. It waited until I’d typed most of the letters of the final word, until I’d definitely committed to the taboo, rather than reminding me of weighty topics when I wasn’t necessarily already thinking about them. With innocuous idioms (like “raining cats and”), the keyboard seemed more proactive about predicting them.

Instead, the goal of text prediction must be to anticipate what the user might want the machine to think they might want to type. For mundane topics, these two goals might seem identical, but their difference shows up as soon as a hint of controversy enters the picture. Predictive text needs to project an aspirational version of a user’s thoughts, a version that avoids subjects like sex and death even though these might be the most important topics to human existence—quite literally the way we enter and leave the world.

We prefer the keyboard to balance raw statistics against our feelings. Sex Death Phone Keyboard is a pretty good name for my future metal band (and a very bad name for my future pony), but I can’t say I’d actually buy a phone that reminds me of my own mortality when I’m composing a grocery list or suggests innuendos when I’m replying to a work email.

The predictive text meme is comforting in a social media world that often leaps from one dismal news cycle to the next. The customizations make us feel seen. The random quirks give our pattern-seeking brains delightful connections. The parts that don’t make sense reassure us of human superiority—the machines can’t be taking over yet if they can’t even write me a decent horoscope! And the topic boundaries prevent the meme from reminding us of our human frailty. The result is a version of ourselves through the verbal equivalent of an Instagram filter, eminently shareable on social media.

https://allthingslinguistic.com/post/182746000314